Entries by Zhikai Liang

Plants can produce metabolites that are directly involved in various biological pathways. Identifying variations of specific metabolites in different genotypes would facilitate the understanding underlying complex genetic architecture of maize. One of most representative natural diversity panels, the 282 Goodman Diversity Panel, was used to perform LC-MS on tips and bases of leaves of each […]

Grain related traits play key roles in determining the total grain yield. However, traditionally manual measurement can not investigate grain seed from multiple angles. To visualize a 3D structure of grain, X-ray based images were captured for a single spike/grain from multiple angles. Because of strong penetration ability, X-ray can not only capture pixels on […]

Advances in sequencing technologies enable scientists to obtain molecular features of genes in high-dimensionality. Features of individual gene like expression, methylation, histone modification, evolutionary signals and sequence itself provide high resolution for distinguishing annotated genes. In plant genomes the percentage of annotated genes with experimental evidence is low. In Moore et al., the authors classified […]

Transposable elements (TE) constitute a large percentage of the maize genome. However, traditional studies based on reference genome alignment can result in large fragments that cannot be aligned, which could be due to TE insertions. The de novo assembly of four representative maize inbreds, B73, W22, Mo17 and PH207, provides an opportunity to compare TEs across genotypes […]

High-throughput plant phenotyping is growing rapidly and enables the collection of dozens or hundreds of traits of the same plant genotype efficiently. The development of this technology expands the diversity of plant phenotypes and brings an opportunity for reexamining the connections between genotype and phenotype from a novel perspective. The widely adopted genome-wide association study […]

The development of deep learning brings opportunities to train computers to solve complex questions. Self-driving vehicles are classic examples of an application of deep learning in the real world. However, the large amounts of data that are required for building accurate models and avoiding overfitting problems were previously hard to accomplish in the plant science area. […]